Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach

Abdul Majeed, Anwar P.P. and Mohd Isa, Wan Hasbullah and Abdul Rauf, Ahmad Ridhauddin and Ab. Nasir, Ahmad Fakhri and Arzmi, Mohd Hafiz and Hafizh, Hadyan and Yap, Eng Hwa (2024) Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach. In: International Conference on Mechatronics and Intelligent Robotics, Suzhou, China.

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Abstract

Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Oral squamous cell carcinoma, Oral cancer, Computer-aided diagnosis, Feature-based transfer learning, Machine learning, Deep learning
Subjects:	R Medicine > RK Dentistry > RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Dentistry > Department of Fundamental Dental and Medical Sciences
Depositing User:	AP Ts Dr Mohd Hafiz Arzmi
Date Deposited:	05 Nov 2024 11:09
Last Modified:	05 Nov 2024 11:09
URI:	http://irep.iium.edu.my/id/eprint/114606

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